Zobrazeno 1 - 10
of 92
pro vyhledávání: '"Shiqiu Peng"'
Publikováno v:
Frontiers in Marine Science, Vol 11 (2024)
Deep learning, a data-driven technology, has attracted widespread attention from various disciplines due to the rapid advancements in the Internet of Things (IoT) big data, machine learning algorithms and computational hardware in recent years. It pr
Externí odkaz:
https://doaj.org/article/96d34f679fd449fead9a44638534ef78
Publikováno v:
Remote Sensing, Vol 16, Iss 13, p 2422 (2024)
Observational data on ocean subsurface temperature and salinity are patently insufficient because in situ observations are complex and costly, while satellite remote-sensed measurements are abundant but mainly focus on sea surface data. To make up fo
Externí odkaz:
https://doaj.org/article/9dd1b9fc23b64a3984215b63e2207438
Publikováno v:
Remote Sensing, Vol 16, Iss 4, p 606 (2024)
Estimating the intensity of tropical cyclones (TCs) is beneficial for preventing and reducing the impact of natural disasters. Most existing methods for estimating TC intensity utilize single-satellite or single-band remote sensing images, but they l
Externí odkaz:
https://doaj.org/article/e5237be9161b495eb8f2be4c808d0820
Publikováno v:
Frontiers in Marine Science, Vol 9 (2022)
Mesoscale eddy prediction has been a big challenge to oceanographers and marine environment forecasters. Although the traditional initialization for the prediction, i.e., through assimilating the satellite-derived sea level anomalies (SLA) into a mod
Externí odkaz:
https://doaj.org/article/4d0ddb4ee57942c6a03f0edc5825f18c
Publikováno v:
Frontiers in Marine Science, Vol 9 (2022)
Accurate wave height prediction is significant in ports, energy, fisheries, and other offshore operations. In this study, a regional significant wave height prediction model with a high spatial and temporal resolution is proposed based on the ConvLST
Externí odkaz:
https://doaj.org/article/e4410cc7630646699b3a1c5e8deb0da7
Publikováno v:
Frontiers in Marine Science, Vol 9 (2022)
An updated real-time Experimental Platform of Marine Environment Forecasting system for the North Indian Ocean, called EPMEF-NIO, is introduced in this study. The main changes of the updated system include the following: 1) aside from the eastern Ind
Externí odkaz:
https://doaj.org/article/b4e05edbb21c4a07b948b599d2869e69
Publikováno v:
Environmental Research Letters, Vol 18, Iss 4, p 044042 (2023)
This study proposes a machine learning approach to probabilistic forecasting of tropical cyclone (TC) intensity. The earth system is complex and nonlinear, leading to inherent uncertainty in TC forecasting at all times, and therefore a representation
Externí odkaz:
https://doaj.org/article/a2549821655e4f73965a42f075ed0622
Publikováno v:
Frontiers in Marine Science, Vol 8 (2021)
Modulations of internal tides (ITs) including the baroclinic tidal energy budget, the incoherency, and the nonlinear interactions among different tidal components by turbulent mixing in the South China Sea (SCS) are investigated through numerical sim
Externí odkaz:
https://doaj.org/article/2420e8246d054cd3977c9294ad6b25e3
Autor:
Zhijuan Lai, Shiqiu Peng
Publikováno v:
Atmosphere, Vol 13, Iss 12, p 1988 (2022)
This study aimed to investigate the effect of assimilating either AMSU-A radiance data from satellites, large-scale flows from the Global Forecast System (GFS), or both together, on improving the track forecast of tropical cyclone (TC). The scale-sel
Externí odkaz:
https://doaj.org/article/39a0f0d9453f422cad90f2b6bce8178e
Publikováno v:
Journal of Physical Oceanography. May2022, Vol. 52 Issue 5, p857-871. 15p.